perf: optimize tag graph response and surface LLM failures

feature/tag-relationship-graph
wuqinchuan 2026-01-11 20:42:14 +08:00
parent 3771de5834
commit 6c16bd0d82
39 changed files with 330 additions and 152 deletions

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@ -13,7 +13,7 @@ Promise.resolve().then(async () => {
<link rel="icon" href="/favicon.ico" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Infinite Image Browsing</title>
<script type="module" crossorigin src="/infinite_image_browsing/fe-static/assets/index-0205daf8.js"></script>
<script type="module" crossorigin src="/infinite_image_browsing/fe-static/assets/index-157194fb.js"></script>
<link rel="stylesheet" href="/infinite_image_browsing/fe-static/assets/index-af514ea9.css">
</head>

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@ -5,15 +5,23 @@ Builds a multi-layer neural-network-style visualization with LLM-driven abstract
import hashlib
import json
import os
import time
from typing import Dict, List, Optional
from pydantic import BaseModel
from fastapi import Depends, FastAPI, HTTPException
from scripts.iib.db.datamodel import DataBase, GlobalSetting
from scripts.iib.tool import normalize_output_lang
from scripts.iib.logger import logger
# Cache version for tag abstraction - increment to invalidate all caches
TAG_ABSTRACTION_CACHE_VERSION = 2
TAG_ABSTRACTION_CACHE_VERSION = 3
TAG_GRAPH_CACHE_VERSION = 1
_MAX_TAGS_FOR_LLM = int(os.getenv("IIB_TAG_GRAPH_MAX_TAGS_FOR_LLM", "200") or "200")
_TOPK_TAGS_FOR_LLM = int(os.getenv("IIB_TAG_GRAPH_TOPK_TAGS_FOR_LLM", "200") or "200")
_LLM_REQUEST_TIMEOUT_SEC = int(os.getenv("IIB_TAG_GRAPH_LLM_TIMEOUT_SEC", "30") or "30")
_LLM_MAX_ATTEMPTS = int(os.getenv("IIB_TAG_GRAPH_LLM_MAX_ATTEMPTS", "2") or "2")
class TagGraphReq(BaseModel):
@ -113,13 +121,14 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
"max_tokens": 2048,
}
# Retry up to 5 times
# Retry a few times then fallback quickly (to avoid frontend timeout on large datasets).
last_error = ""
for attempt in range(1, 6):
for attempt in range(1, _LLM_MAX_ATTEMPTS + 1):
try:
resp = requests.post(url, json=payload, headers=headers, timeout=60)
resp = requests.post(url, json=payload, headers=headers, timeout=_LLM_REQUEST_TIMEOUT_SEC)
except requests.RequestException as e:
last_error = f"network_error: {type(e).__name__}: {e}"
logger.warning("[tag_graph] llm_request_error attempt=%s err=%s", attempt, last_error)
continue
# Retry on 429 or 5xx, fail immediately on other 4xx
@ -127,14 +136,17 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
body = (resp.text or "")[:400]
if resp.status_code == 429 or resp.status_code >= 500:
last_error = f"api_error_retriable: status={resp.status_code}"
logger.warning("[tag_graph] llm_http_error attempt=%s status=%s body=%s", attempt, resp.status_code, body)
continue
# 4xx client error - fail immediately
logger.error("[tag_graph] llm_http_client_error attempt=%s status=%s body=%s", attempt, resp.status_code, body)
raise Exception(f"API client error: {resp.status_code} {body}")
try:
data = resp.json()
except Exception as e:
last_error = f"response_not_json: {type(e).__name__}"
logger.warning("[tag_graph] llm_response_not_json attempt=%s err=%s body=%s", attempt, last_error, (resp.text or "")[:400])
continue
choice0 = (data.get("choices") or [{}])[0]
@ -164,6 +176,7 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
if not json_str:
last_error = f"no_json_found: {content[:200]}"
logger.warning("[tag_graph] llm_no_json attempt=%s err=%s", attempt, last_error)
continue
# Clean up common JSON issues
@ -175,55 +188,34 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
result = json.loads(json_str)
except json.JSONDecodeError as e:
last_error = f"json_parse_error: {e}"
logger.warning("[tag_graph] llm_json_parse_error attempt=%s err=%s json=%s", attempt, last_error, json_str[:400])
continue
# Validate structure
if not isinstance(result, dict) or "layers" not in result:
last_error = f"invalid_structure: {str(result)[:200]}"
logger.warning("[tag_graph] llm_invalid_structure attempt=%s err=%s", attempt, last_error)
continue
# Success!
return result
# All retries exhausted
# Fallback: create simple heuristic grouping
# Group by first character/prefix for basic organization
groups_dict = {}
for tag in tags:
# Simple heuristic: group by first 2 chars or common keywords
prefix = tag[:2] if len(tag) >= 2 else tag
if prefix not in groups_dict:
groups_dict[prefix] = []
groups_dict[prefix].append(tag)
# Merge small groups
merged_groups = []
for prefix, tag_list in sorted(groups_dict.items()):
if len(merged_groups) > 0 and len(tag_list) < 3:
# Merge into last group if this group is too small
merged_groups[-1]["tags"].extend(tag_list)
else:
group_id = f"group{len(merged_groups) + 1}"
label = f"Group {len(merged_groups) + 1}"
merged_groups.append({
"id": group_id,
"label": label,
"tags": tag_list
})
# Limit to max 12 groups
if len(merged_groups) > 12:
# Merge smallest groups
merged_groups = sorted(merged_groups, key=lambda g: len(g["tags"]), reverse=True)[:12]
return {
"layers": [
{
"level": 1,
"groups": merged_groups
}
]
}
# No fallback: expose error to frontend, but log enough info for debugging.
logger.error(
"[tag_graph] llm_failed attempts=%s timeout_sec=%s last_error=%s",
_LLM_MAX_ATTEMPTS,
_LLM_REQUEST_TIMEOUT_SEC,
last_error,
)
raise HTTPException(
status_code=500,
detail={
"type": "tag_graph_llm_failed",
"attempts": _LLM_MAX_ATTEMPTS,
"timeout_sec": _LLM_REQUEST_TIMEOUT_SEC,
"last_error": last_error,
},
)
return await asyncio.to_thread(_call_sync)
@ -241,6 +233,7 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
- Layer 1: Tag nodes (deduplicated cluster keywords)
- Layer 2+: Abstract groupings (LLM-generated, max 2 layers)
"""
t0 = time.time()
# Validate
if not req.folder_paths:
raise HTTPException(400, "folder_paths is required")
@ -252,21 +245,25 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
from contextlib import closing
with closing(conn.cursor()) as cur:
# Avoid full table scan on large DBs; we only need recent caches for matching.
cur.execute(
"""SELECT cache_key, folders, result FROM topic_cluster_cache
ORDER BY updated_at DESC"""
ORDER BY updated_at DESC
LIMIT 200"""
)
rows = cur.fetchall()
# Find a cache that matches the folders (order-independent)
folders_set = set(folders)
row = None
topic_cluster_cache_key: Optional[str] = None
for cache_row in rows:
try:
cached_folders = json.loads(cache_row[1]) if isinstance(cache_row[1], str) else cache_row[1]
if isinstance(cached_folders, list) and set(cached_folders) == folders_set:
row = (cache_row[0], cache_row[2])
topic_cluster_cache_key = str(cache_row[0] or "")
row = (topic_cluster_cache_key, cache_row[2])
break
except Exception:
continue
@ -288,6 +285,45 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
if not clusters:
raise HTTPException(400, "No clusters found in result")
logger.info(
"[tag_graph] start folders=%s clusters=%s lang=%s",
len(folders),
len(clusters) if isinstance(clusters, list) else "n/a",
str(req.lang or ""),
)
# Graph cache (best-effort): cache by topic_cluster_cache_key + lang + version.
graph_cache_key = None
if topic_cluster_cache_key:
try:
graph_cache_key_hash = hashlib.md5(
json.dumps(
{
"v": TAG_GRAPH_CACHE_VERSION,
"topic_cluster_cache_key": topic_cluster_cache_key,
"lang": str(req.lang or ""),
},
ensure_ascii=False,
sort_keys=True,
).encode("utf-8")
).hexdigest()
graph_cache_key = f"tag_graph_v{TAG_GRAPH_CACHE_VERSION}_{graph_cache_key_hash}"
cached_graph = GlobalSetting.get_setting(conn, graph_cache_key)
if cached_graph:
cached_graph_obj = json.loads(cached_graph) if isinstance(cached_graph, str) else cached_graph
if isinstance(cached_graph_obj, dict) and "layers" in cached_graph_obj and "links" in cached_graph_obj:
cached_graph_obj.setdefault("stats", {})
if isinstance(cached_graph_obj["stats"], dict):
cached_graph_obj["stats"].setdefault("topic_cluster_cache_key", topic_cluster_cache_key)
logger.info(
"[tag_graph] cache_hit topic_cluster_cache_key=%s cost_ms=%s",
topic_cluster_cache_key,
int((time.time() - t0) * 1000),
)
return cached_graph_obj
except Exception:
pass
# === Layer 0: Cluster Nodes ===
top_clusters = sorted(clusters, key=lambda c: c.get("size", 0), reverse=True)
@ -299,8 +335,6 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
cluster_title = cluster.get("title", "Untitled")
cluster_size = cluster.get("size", 0)
keywords = cluster.get("keywords", [])
paths = cluster.get("paths", [])
node_id = f"cluster_{cluster_id}"
cluster_nodes.append(LayerNode(
id=node_id,
@ -309,7 +343,8 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
metadata={
"type": "cluster",
"image_count": cluster_size,
"paths": paths
# Do NOT include full paths list here (can be huge); fetch on demand.
"cluster_id": cluster_id,
}
))
@ -382,14 +417,41 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
abstract_layers = []
layer1_to_2_links = []
if len(selected_tags) > 3: # Only abstract if we have enough tags
# LLM abstraction: for large datasets, do TopK by total_images (frequency/weight) instead of skipping.
# This keeps response useful while controlling LLM latency and prompt size.
llm_tags = [t for (t, _stats) in sorted_tags][: max(0, int(_TOPK_TAGS_FOR_LLM))]
llm_tags_set = set(llm_tags)
# IMPORTANT: Do not gate LLM by total tag count; only use TopK tags for LLM input.
# Otherwise, large datasets would "skip" abstraction even though we already limited llm_tags.
should_use_llm = (
len(llm_tags) > 3
and len(llm_tags) <= _MAX_TAGS_FOR_LLM
and bool(openai_api_key)
and bool(openai_base_url)
)
if not should_use_llm:
logger.info(
"[tag_graph] llm_disabled reasons={topk_tags:%s,max:%s,has_key:%s,has_base:%s}",
len(llm_tags),
_MAX_TAGS_FOR_LLM,
bool(openai_api_key),
bool(openai_base_url),
)
logger.info(
"[tag_graph] tags_total=%s tags_for_llm=%s llm_enabled=%s",
len(selected_tags),
len(llm_tags),
bool(should_use_llm),
)
if should_use_llm:
# Use language from request
lang = req.lang or "en"
# Generate cache key for this set of tags (with version)
import hashlib
tags_sorted = sorted(selected_tags)
cache_input = f"v{TAG_ABSTRACTION_CACHE_VERSION}|{ai_model}|{lang}|{','.join(tags_sorted)}"
tags_sorted = sorted(llm_tags_set)
cache_input = f"v{TAG_ABSTRACTION_CACHE_VERSION}|{ai_model}|{lang}|topk={len(tags_sorted)}|{','.join(tags_sorted)}"
cache_key_hash = hashlib.md5(cache_input.encode()).hexdigest()
cache_key = f"tag_abstraction_v{TAG_ABSTRACTION_CACHE_VERSION}_{cache_key_hash}"
@ -404,13 +466,23 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
# Call LLM if not cached
if not abstraction:
abstraction = await _call_llm_for_abstraction(
list(selected_tags),
lang,
ai_model,
openai_base_url,
openai_api_key
)
t_llm = time.time()
try:
abstraction = await _call_llm_for_abstraction(
list(llm_tags_set),
lang,
ai_model,
openai_base_url,
openai_api_key
)
except Exception as e:
logger.exception(
"[tag_graph] llm_call_failed cost_ms=%s err=%s",
int((time.time() - t_llm) * 1000),
f"{type(e).__name__}: {e}",
)
raise
logger.info("[tag_graph] llm_done cost_ms=%s cached=%s", int((time.time() - t_llm) * 1000), False)
# Save to cache if successful
if abstraction and isinstance(abstraction, dict) and abstraction.get("layers"):
@ -418,6 +490,8 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
GlobalSetting.save_setting(conn, cache_key, json.dumps(abstraction, ensure_ascii=False))
except Exception:
pass
else:
logger.info("[tag_graph] llm_done cost_ms=%s cached=%s", 0, True)
# Build abstract layers from LLM response
llm_layers = abstraction.get("layers", [])
@ -443,7 +517,7 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
total_size = sum(
tag_stats.get(tag, {}).get("total_images", 0)
for tag in contained_tags
if tag in selected_tags
if tag in llm_tags_set
)
abstract_nodes.append(LayerNode(
@ -455,7 +529,7 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
# Create links from tags to this abstract node
for tag in contained_tags:
if tag in selected_tags:
if tag in llm_tags_set:
tag_id = f"tag_{tag}"
layer1_to_2_links.append(GraphLink(
source=tag_id,
@ -511,15 +585,79 @@ If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }
all_links = [link.dict() for link in (layer0_to_1_links + layer1_to_2_links)]
return TagGraphResp(
stats = {
"total_clusters": len(clusters),
"selected_clusters": len(cluster_nodes),
"total_tags": len(tag_stats),
"selected_tags": len(tag_nodes),
"abstraction_layers": len(abstract_layers),
"total_links": len(all_links),
}
if topic_cluster_cache_key:
stats["topic_cluster_cache_key"] = topic_cluster_cache_key
resp_obj = TagGraphResp(
layers=layers,
links=all_links,
stats={
"total_clusters": len(clusters),
"selected_clusters": len(cluster_nodes),
"total_tags": len(tag_stats),
"selected_tags": len(tag_nodes),
"abstraction_layers": len(abstract_layers),
"total_links": len(all_links),
}
stats=stats,
).dict()
# Save graph cache (best-effort)
if graph_cache_key:
try:
GlobalSetting.save_setting(conn, graph_cache_key, json.dumps(resp_obj, ensure_ascii=False))
conn.commit()
except Exception:
pass
logger.info(
"[tag_graph] done nodes=%s links=%s abstraction_layers=%s cost_ms=%s",
sum(len(l.get("nodes") or []) for l in (resp_obj.get("layers") or []) if isinstance(l, dict)),
len(resp_obj.get("links") or []),
int((resp_obj.get("stats") or {}).get("abstraction_layers") or 0),
int((time.time() - t0) * 1000),
)
return resp_obj
class ClusterPathsReq(BaseModel):
topic_cluster_cache_key: str
cluster_id: str
@app.post(
f"{db_api_base}/cluster_tag_graph_cluster_paths",
dependencies=[Depends(verify_secret)],
)
async def cluster_tag_graph_cluster_paths(req: ClusterPathsReq):
"""
Fetch full paths for a specific cluster from cached clustering result.
This avoids returning huge "paths" arrays inside cluster_tag_graph response.
"""
ck = str(req.topic_cluster_cache_key or "").strip()
cid = str(req.cluster_id or "").strip()
if not ck or not cid:
raise HTTPException(400, "topic_cluster_cache_key and cluster_id are required")
conn = DataBase.get_conn()
from contextlib import closing
with closing(conn.cursor()) as cur:
cur.execute("SELECT result FROM topic_cluster_cache WHERE cache_key = ?", (ck,))
row = cur.fetchone()
if not row:
raise HTTPException(404, "topic_cluster_cache not found")
try:
result_obj = json.loads(row[0]) if isinstance(row[0], str) else row[0]
except Exception:
result_obj = None
if not isinstance(result_obj, dict):
raise HTTPException(400, "invalid cached result")
clusters = result_obj.get("clusters", [])
if not isinstance(clusters, list):
clusters = []
for c in clusters:
if not isinstance(c, dict):
continue
if str(c.get("id", "")) == cid:
paths = c.get("paths", [])
if not isinstance(paths, list):
paths = []
return {"paths": [str(p) for p in paths if p]}
raise HTTPException(404, "cluster not found")

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@ -1 +1 @@
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2
vue/dist/index.html vendored
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@ -7,7 +7,7 @@
<link rel="icon" href="/favicon.ico" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Infinite Image Browsing</title>
<script type="module" crossorigin src="/infinite_image_browsing/fe-static/assets/index-0205daf8.js"></script>
<script type="module" crossorigin src="/infinite_image_browsing/fe-static/assets/index-157194fb.js"></script>
<link rel="stylesheet" href="/infinite_image_browsing/fe-static/assets/index-af514ea9.css">
</head>

View File

@ -350,10 +350,26 @@ export interface TagGraphResp {
selected_tags: number
abstraction_layers: number
total_links: number
topic_cluster_cache_key?: string
}
}
export const getClusterTagGraph = async (req: TagGraphReq) => {
const resp = await axiosInst.value.post('/db/cluster_tag_graph', req, { timeout: 60000 })
// Large datasets can take longer to build / transfer; keep a generous timeout.
const resp = await axiosInst.value.post('/db/cluster_tag_graph', req, { timeout: 300000 })
return resp.data as TagGraphResp
}
export interface TagGraphClusterPathsReq {
topic_cluster_cache_key: string
cluster_id: string
}
export interface TagGraphClusterPathsResp {
paths: string[]
}
export const getClusterTagGraphClusterPaths = async (req: TagGraphClusterPathsReq) => {
const resp = await axiosInst.value.post('/db/cluster_tag_graph_cluster_paths', req, { timeout: 300000 })
return resp.data as TagGraphClusterPathsResp
}

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@ -67,7 +67,7 @@
<script setup lang="ts">
import { computed, ref, onMounted, watch, nextTick, onUnmounted } from 'vue'
import { getClusterTagGraph, type TagGraphReq, type TagGraphResp } from '@/api/db'
import { getClusterTagGraph, getClusterTagGraphClusterPaths, type TagGraphReq, type TagGraphResp } from '@/api/db'
import { message } from 'ant-design-vue'
import * as echarts from 'echarts'
import { t } from '@/i18n'
@ -166,7 +166,13 @@ const fetchGraphData = async () => {
graphData.value = await getClusterTagGraph(req)
displayGraphData.value = graphData.value
} catch (err: any) {
error.value = err.response?.data?.detail || err.message || 'Failed to load graph'
const detail = err.response?.data?.detail
error.value =
typeof detail === 'string'
? detail
: detail
? JSON.stringify(detail)
: (err.message || 'Failed to load graph')
message.error(error.value)
} finally {
loading.value = false
@ -585,15 +591,33 @@ const renderChart = () => {
hideTipOnly()
} else if (action === 'openCluster' && idx != null) {
const meta = (nodes[idx] && nodes[idx].metadata) || {}
const paths = (meta.paths || []) as string[]
const title = (nodes[idx] && nodes[idx].name) || ''
const size = Number(meta.image_count || paths.length || 0)
if (paths && paths.length) {
emit('openCluster', { title, paths, size })
} else {
message.warning('No images found in this cluster')
const size = Number(meta.image_count || 0)
const topicClusterCacheKey = String(displayGraphData.value?.stats?.topic_cluster_cache_key || '')
const clusterId = String(meta.cluster_id || '')
if (!topicClusterCacheKey || !clusterId) {
message.warning('Cluster data is incomplete, please re-generate clustering result')
hideTipOnly()
return
}
hideTipOnly()
void (async () => {
const hide = message.loading('Loading cluster images...', 0)
try {
const resp = await getClusterTagGraphClusterPaths({
topic_cluster_cache_key: topicClusterCacheKey,
cluster_id: clusterId
})
const paths: string[] = Array.isArray(resp?.paths) ? resp.paths : []
if (paths.length) {
emit('openCluster', { title, paths, size: size || paths.length })
} else {
message.warning('No images found in this cluster')
}
hideTipOnly()
} finally {
hide?.()
}
})()
} else if (action === 'close') {
hideTipOnly()
}